The monitoring of customer traffic in a retail establishment provides valuable information to the management of these establishments. Management can evaluate the effectiveness of advertisements, promotions and events by monitoring the number of customers entering and exiting the retail establishment at certain times. In addition, this information can serve as the basis for staffing and security decisions. Furthermore, accurate customer traffic information can be extremely useful in management discussions with tenants.
Customer traffic information has been obtained in several ways. Individuals can manually count and record the number of people entering a retail establishment. This method, however, is expensive and varies in accuracy depending on the individual. Turnstiles have traditionally provided customer counting information, but turnstiles are inconvenient for many retail applications. Surprisingly, the most common method of obtaining customer information in a retail environment is by car counting.
Car counting involves placing a pneumatic tube or inductive loop at the entrance to a retail establishment that detects cars entering the retail parking area. The customer count is derived from the car count multiplied by an estimate of the number of people per car. Another common technique involves using vertical or horizontal light beams at the entrance to the retail establishment. This technique counts people as they enter the retail establishment and break the light beams, but this technique can be inaccurate. Recently, video imaging or visual imaging systems have been developed that also provide customer traffic information.
Video imaging generally involves the conversion of an image into an array of pixels. Each pixel contains information describing a small component or area of the entire image. By analyzing present and past pixel arrays associated with a particular image, the detection of an object within the particular image and a direction of movement associated with that object is possible.
The science of computer vision has provided a number of approaches to the interesting problem of detecting and tracking moving objects. Some of the tools or algorithms used are auto-correlation, optical flow, edge enhancement, and segmentation pattern matching to mention a few. Most of these approaches deal with the overall scene. As such, two major problems must be solved:
(1) Search--the objects of interest must be identified and isolated as individual targets; and PA1 (2) Track--the objects must be traced from their previous position to their present position.
The ease with which the human can perform these tasks belies the difficulty involved to make a computer perform the same task. The human has such a rich abundance of cues to distinguish subtle differences that a human can look away from the scene and then return and identify the location of an object of interest very quickly. The video processor, however, must first determine whether an object of interest actually exists in the image. Once the video processor locates an object of interest, the video processor must track the object of interest through the maze of other objects while being given a constant stream of updates. Unfortunately, the task of searching and tracking objects in this manner requires a lot of computing power which translates into increased costs.
Some visual imaging systems perform object detection and determine the direction of movement for an object at a relatively low cost without powerful computing systems. These systems, however, are quite limited in application. For example, certain systems are limited to detecting objects, such as people, one person at a time. In this way, by limiting the complexity of the system application, these visual imaging systems do not require powerful computing, but these systems cannot function in an environment of multiple objects simultaneously traversing a traffic zone in different directions, such as in a retail environment. Additionally, these systems must perform conventional blob analysis to differentiate between different types of objects, adding to the computing power required. Thus, a need exists for providing a video imaging system that can simultaneously detect a certain type of object, such as people, and determine the directions of motion for those objects but does not require the computing power of previous video imaging systems.